Bagging, Boosting and the Random Subspace Method for Linear Classifiers

Simulation studies show that the performance of the combining techniques is strongly affected by the small sample size properties of the base classifier: boosting is useful for large training sample sizes, while bagging and the random subspace method are useful for criticalTraining sample sizes.